【Week-R1】RNN实现心脏病预测，基于tensorflow框架

    import tensorflow as tf
    
    gpus = tf.config.list_physical_devices("GPU")
    
    if gpus:
    gpu0 = gpus[0]
    tf.config.experimental.set_memory_growth(gpu0,true)
    tf.config.set_visible_devices([gpu0],"GPU")
    print("GPU: ",gpus)
    else:
    print("CPU:")
    
    
    # 2.1 导入数据
    import numpy as np
    import pandas as pd
    
    df = pd.read_csv("D:\ jupyter notebook\ DL-100-days\ RNN\ heart.csv")
    print("df: ", df)
    
    # 2.2 检查是否有空值
    df.isnull().sum()

    #3.1 划分训练集与测试集
    from sklearn.preprocessing import StandardScaler
    from sklearn.model_selection import train_test_split
    
    x = df.iloc[:, :-1]
    y = df.iloc[:, -1]
    x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.1,random_state=1)
    print("x_train.shape: ", x_train.shape)
    print("y_train.shape: ", y_train.shape)
    
    # 3.2 标准化: 针对每一列进行标准化
    sc = StandardScaler()
    x_train = sc.fit_transform(x_train)
    x_test = sc.transform(x_test)
    
    x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], 1)
    x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], 1)

    ● units: 正整数，输出空间的维度。
    
    ● activation: 要使用的激活函数。 默认：双曲正切（tanh）。 如果传入 None，则不使用激活函数 (即 线性激活：a(x) = x)。
    
    ● use_bias: 布尔值，该层是否使用偏置向量。
    
    ● kernel_initializer: kernel 权值矩阵的初始化器， 用于输入的线性转换 (详见 initializers)。
    
    ● recurrent_initializer: recurrent_kernel 权值矩阵 的初始化器，用于循环层状态的线性转换 (详见 initializers)。
    
    ● bias_initializer:偏置向量的初始化器 (详见initializers).
    
    ● dropout: 在 0 和 1 之间的浮点数。 单元的丢弃比例，用于输入的线性转换。

    # 4.1 构建RNN模型
    import keras
    from keras.models import Sequential
    from keras.layers import Dense,LSTM,SimpleRNN
    
    model = Sequential()
    model.add(SimpleRNN(200, input_shape=(13,1), activation='relu'))
    model.add(Dense(100, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    print(model.summary())

    #4.2 编译模型
    opt = tf.keras.optimizers.Adam(learning_rate=1e-4)
    model.compile(loss='binary_crossentropy',
              optimizer = opt,
              metrics="accuracy")
    
    # 4.3 训练模型 
    epochs = 100
    history = model.fit(x_train, y_train,
                    epochs=epochs,
                    batch_size=128,
                    validation_data=(x_test,y_test),
                    verbose=1)

    #4.4 评估模型 
    import matplotlib.pyplot as plt
    acc = history.history['accuracy']
    val_acc = history.history['val_accuracy']
    
    loss = history.history['loss']
    val_loss = history.history['val_loss']
    
    epochs_range = range(epochs)
    
    plt.figure(figsize=(14,4))
    plt.subplot(1,2,1)
    plt.plot(epochs_range, acc, label='Training Acuuracy')
    plt.plot(epochs_range, val_acc, label='Validation Accuracy')
    plt.legend(loc='lower right')
    plt.title('Training & Validation Accuracy')
    
    plt.subplot(1,2,2)
    plt.plot(epochs_range, loss, label='Training Loss')
    plt.plot(epochs_range, val_loss, label='Validation Losss')
    plt.legend(loc='upper right')
    plt.title('Training & Validation Loss')
    
    plt.savefig("D:\ jupyter notebook\ DL-100-days\ RNN\ result.png")
    plt.show()
    
    
    scores = model.evaluate(x_test,y_test,verbose=0)
    print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))